{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Learning & Art: Neural Style Transfer\n",
    "\n",
    "Welcome to the second assignment of this week. In this assignment, you will learn about Neural Style Transfer. This algorithm was created by Gatys et al. (2015) (https://arxiv.org/abs/1508.06576). \n",
    "\n",
    "**In this assignment, you will:**\n",
    "- Implement the neural style transfer algorithm \n",
    "- Generate novel artistic images using your algorithm \n",
    "\n",
    "Most of the algorithms you've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, you'll optimize a cost function to get pixel values!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import scipy.io\n",
    "import scipy.misc\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "from PIL import Image\n",
    "from nst_utils import *\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Problem Statement\n",
    "\n",
    "Neural Style Transfer (NST) is one of the most fun techniques in deep learning. As seen below, it merges two images, namely, a \"content\" image (C) and a \"style\" image (S), to create a \"generated\" image (G). The generated image G combines the \"content\" of the image C with the \"style\" of image S. \n",
    "\n",
    "In this example, you are going to generate an image of the Louvre museum in Paris (content image C), mixed with a painting by Claude Monet, a leader of the impressionist movement (style image S).\n",
    "<img src=\"images/louvre_generated.png\" style=\"width:750px;height:200px;\">\n",
    "\n",
    "Let's see how you can do this. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Transfer Learning\n",
    "\n",
    "Neural Style Transfer (NST) uses a previously trained convolutional network, and builds on top of that. The idea of using a network trained on a different task and applying it to a new task is called transfer learning. \n",
    "\n",
    "Following the original NST paper (https://arxiv.org/abs/1508.06576), we will use the VGG network. Specifically, we'll use VGG-19, a 19-layer version of the VGG network. This model has already been trained on the very large ImageNet database, and thus has learned to recognize a variety of low level features (at the earlier layers) and high level features (at the deeper layers). \n",
    "\n",
    "Run the following code to load parameters from the VGG model. This may take a few seconds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'input': <tf.Variable 'Variable:0' shape=(1, 300, 400, 3) dtype=float32_ref>, 'conv1_1': <tf.Tensor 'Relu:0' shape=(1, 300, 400, 64) dtype=float32>, 'conv1_2': <tf.Tensor 'Relu_1:0' shape=(1, 300, 400, 64) dtype=float32>, 'avgpool1': <tf.Tensor 'AvgPool:0' shape=(1, 150, 200, 64) dtype=float32>, 'conv2_1': <tf.Tensor 'Relu_2:0' shape=(1, 150, 200, 128) dtype=float32>, 'conv2_2': <tf.Tensor 'Relu_3:0' shape=(1, 150, 200, 128) dtype=float32>, 'avgpool2': <tf.Tensor 'AvgPool_1:0' shape=(1, 75, 100, 128) dtype=float32>, 'conv3_1': <tf.Tensor 'Relu_4:0' shape=(1, 75, 100, 256) dtype=float32>, 'conv3_2': <tf.Tensor 'Relu_5:0' shape=(1, 75, 100, 256) dtype=float32>, 'conv3_3': <tf.Tensor 'Relu_6:0' shape=(1, 75, 100, 256) dtype=float32>, 'conv3_4': <tf.Tensor 'Relu_7:0' shape=(1, 75, 100, 256) dtype=float32>, 'avgpool3': <tf.Tensor 'AvgPool_2:0' shape=(1, 38, 50, 256) dtype=float32>, 'conv4_1': <tf.Tensor 'Relu_8:0' shape=(1, 38, 50, 512) dtype=float32>, 'conv4_2': <tf.Tensor 'Relu_9:0' shape=(1, 38, 50, 512) dtype=float32>, 'conv4_3': <tf.Tensor 'Relu_10:0' shape=(1, 38, 50, 512) dtype=float32>, 'conv4_4': <tf.Tensor 'Relu_11:0' shape=(1, 38, 50, 512) dtype=float32>, 'avgpool4': <tf.Tensor 'AvgPool_3:0' shape=(1, 19, 25, 512) dtype=float32>, 'conv5_1': <tf.Tensor 'Relu_12:0' shape=(1, 19, 25, 512) dtype=float32>, 'conv5_2': <tf.Tensor 'Relu_13:0' shape=(1, 19, 25, 512) dtype=float32>, 'conv5_3': <tf.Tensor 'Relu_14:0' shape=(1, 19, 25, 512) dtype=float32>, 'conv5_4': <tf.Tensor 'Relu_15:0' shape=(1, 19, 25, 512) dtype=float32>, 'avgpool5': <tf.Tensor 'AvgPool_4:0' shape=(1, 10, 13, 512) dtype=float32>}\n"
     ]
    }
   ],
   "source": [
    "model = load_vgg_model(\"pretrained-model/imagenet-vgg-verydeep-19.mat\")\n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is stored in a python dictionary where each variable name is the key and the corresponding value is a tensor containing that variable's value. To run an image through this network, you just have to feed the image to the model. In TensorFlow, you can do so using the [tf.assign](https://www.tensorflow.org/api_docs/python/tf/assign) function. In particular, you will use the assign function like this:  \n",
    "```python\n",
    "model[\"input\"].assign(image)\n",
    "```\n",
    "This assigns the image as an input to the model. After this, if you want to access the activations of a particular layer, say layer `4_2` when the network is run on this image, you would run a TensorFlow session on the correct tensor `conv4_2`, as follows:  \n",
    "```python\n",
    "sess.run(model[\"conv4_2\"])\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Neural Style Transfer \n",
    "\n",
    "We will build the NST algorithm in three steps:\n",
    "\n",
    "- Build the content cost function $J_{content}(C,G)$\n",
    "- Build the style cost function $J_{style}(S,G)$\n",
    "- Put it together to get $J(G) = \\alpha J_{content}(C,G) + \\beta J_{style}(S,G)$. \n",
    "\n",
    "### 3.1 - Computing the content cost\n",
    "\n",
    "In our running example, the content image C will be the picture of the Louvre Museum in Paris. Run the code below to see a picture of the Louvre."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22a23192dd8>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a23188ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "content_image = scipy.misc.imread(\"images/louvre.jpg\")\n",
    "imshow(content_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The content image (C) shows the Louvre museum's pyramid surrounded by old Paris buildings, against a sunny sky with a few clouds.\n",
    "\n",
    "** 3.1.1 - How do you ensure the generated image G matches the content of the image C?**\n",
    "\n",
    "As we saw in lecture, the earlier (shallower) layers of a ConvNet tend to detect lower-level features such as edges and simple textures, and the later (deeper) layers tend to detect higher-level features such as more complex textures as well as object classes. \n",
    "\n",
    "We would like the \"generated\" image G to have similar content as the input image C. Suppose you have chosen some layer's activations to represent the content of an image. In practice, you'll get the most visually pleasing results if you choose a layer in the middle of the network--neither too shallow nor too deep. (After you have finished this exercise, feel free to come back and experiment with using different layers, to see how the results vary.)\n",
    "\n",
    "So, suppose you have picked one particular hidden layer to use. Now, set the image C as the input to the pretrained VGG network, and run forward propagation. Let $a^{(C)}$ be the hidden layer activations in the layer you had chosen. (In lecture, we had written this as $a^{[l](C)}$, but here we'll drop the superscript $[l]$ to simplify the notation.) This will be a $n_H \\times n_W \\times n_C$ tensor. Repeat this process with the image G: Set G as the input, and run forward progation. Let $$a^{(G)}$$ be the corresponding hidden layer activation. We will define as the content cost function as:\n",
    "\n",
    "$$J_{content}(C,G) =  \\frac{1}{4 \\times n_H \\times n_W \\times n_C}\\sum _{ \\text{all entries}} (a^{(C)} - a^{(G)})^2\\tag{1} $$\n",
    "\n",
    "Here, $n_H, n_W$ and $n_C$ are the height, width and number of channels of the hidden layer you have chosen, and appear in a normalization term in the cost. For clarity, note that $a^{(C)}$ and $a^{(G)}$ are the volumes corresponding to a hidden layer's activations. In order to compute the cost $J_{content}(C,G)$, it might also be convenient to unroll these 3D volumes into a 2D matrix, as shown below. (Technically this unrolling step isn't needed to compute $J_{content}$, but it will be good practice for when you do need to carry out a similar operation later for computing the style const $J_{style}$.)\n",
    "\n",
    "<img src=\"images/NST_LOSS.png\" style=\"width:800px;height:400px;\">\n",
    "\n",
    "**Exercise:** Compute the \"content cost\" using TensorFlow. \n",
    "\n",
    "**Instructions**: The 3 steps to implement this function are:\n",
    "1. Retrieve dimensions from a_G: \n",
    "    - To retrieve dimensions from a tensor X, use: `X.get_shape().as_list()`\n",
    "2. Unroll a_C and a_G as explained in the picture above\n",
    "    - If you are stuck, take a look at [Hint1](https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose) and [Hint2](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/reshape).\n",
    "3. Compute the content cost:\n",
    "    - If you are stuck, take a look at [Hint3](https://www.tensorflow.org/api_docs/python/tf/reduce_sum), [Hint4](https://www.tensorflow.org/api_docs/python/tf/square) and [Hint5](https://www.tensorflow.org/api_docs/python/tf/subtract)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_content_cost\n",
    "\n",
    "def compute_content_cost(a_C, a_G):\n",
    "    \"\"\"\n",
    "    Computes the content cost\n",
    "    \n",
    "    Arguments:\n",
    "    a_C -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image C \n",
    "    a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing content of the image G\n",
    "    \n",
    "    Returns: \n",
    "    J_content -- scalar that you compute using equation 1 above.\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    # Retrieve dimensions from a_G (≈1 line)\n",
    "    m, n_H, n_W, n_C = a_G.get_shape().as_list()\n",
    "    \n",
    "    # Reshape a_C and a_G (≈2 lines)\n",
    "    a_C_unrolled = tf.reshape(a_C,(n_H * n_W,n_C))\n",
    "    a_G_unrolled = tf.reshape(a_G,(n_H * n_W,n_C))\n",
    "    \n",
    "    # compute the cost with tensorflow (≈1 line)\n",
    "    J_content = 1/(4*n_H*n_W*n_C) * tf.reduce_sum(tf.square(tf.subtract(a_C_unrolled , a_G_unrolled)))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return J_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J_content = 6.76559\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph() \n",
    "\n",
    "with tf.Session() as test:\n",
    "    tf.set_random_seed(1)\n",
    "    a_C = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n",
    "    a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n",
    "    J_content = compute_content_cost(a_C, a_G)\n",
    "    print(\"J_content = \" + str(J_content.eval()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **J_content**\n",
    "        </td>\n",
    "        <td>\n",
    "           6.76559\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The content cost takes a hidden layer activation of the neural network, and measures how different $a^{(C)}$ and $a^{(G)}$ are. \n",
    "- When we minimize the content cost later, this will help make sure $G$ has similar content as $C$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Computing the style cost\n",
    "\n",
    "For our running example, we will use the following style image: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x22a23219a90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22a29862f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style_image = scipy.misc.imread(\"images/monet_800600.jpg\")\n",
    "imshow(style_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This painting was painted in the style of *[impressionism](https://en.wikipedia.org/wiki/Impressionism)*.\n",
    "\n",
    "Lets see how you can now define a \"style\" const function $J_{style}(S,G)$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.1 - Style matrix\n",
    "\n",
    "The style matrix is also called a \"Gram matrix.\" In linear algebra, the Gram matrix G of a set of vectors $(v_{1},\\dots ,v_{n})$ is the matrix of dot products, whose entries are ${\\displaystyle G_{ij} = v_{i}^T v_{j} = np.dot(v_{i}, v_{j})  }$. In other words, $G_{ij}$ compares how similar $v_i$ is to $v_j$: If they are highly similar, you would expect them to have a large dot product, and thus for $G_{ij}$ to be large. \n",
    "\n",
    "Note that there is an unfortunate collision in the variable names used here. We are following common terminology used in the literature, but $G$ is used to denote the Style matrix (or Gram matrix) as well as to denote the generated image $G$. We will try to make sure which $G$ we are referring to is always clear from the context. \n",
    "\n",
    "In NST, you can compute the Style matrix by multiplying the \"unrolled\" filter matrix with their transpose:\n",
    "\n",
    "<img src=\"images/NST_GM.png\" style=\"width:900px;height:300px;\">\n",
    "\n",
    "The result is a matrix of dimension $(n_C,n_C)$ where $n_C$ is the number of filters. The value $G_{ij}$ measures how similar the activations of filter $i$ are to the activations of filter $j$. \n",
    "\n",
    "One important part of the gram matrix is that the diagonal elements such as $G_{ii}$ also measures how active filter $i$ is. For example, suppose filter $i$ is detecting vertical textures in the image. Then $G_{ii}$ measures how common  vertical textures are in the image as a whole: If $G_{ii}$ is large, this means that the image has a lot of vertical texture. \n",
    "\n",
    "By capturing the prevalence of different types of features ($G_{ii}$), as well as how much different features occur together ($G_{ij}$), the Style matrix $G$ measures the style of an image. \n",
    "\n",
    "**Exercise**:\n",
    "Using TensorFlow, implement a function that computes the Gram matrix of a matrix A. The formula is: The gram matrix of A is $G_A = AA^T$. If you are stuck, take a look at [Hint 1](https://www.tensorflow.org/api_docs/python/tf/matmul) and [Hint 2](https://www.tensorflow.org/api_docs/python/tf/transpose)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: gram_matrix\n",
    "\n",
    "def gram_matrix(A):\n",
    "    \"\"\"\n",
    "    Argument:\n",
    "    A -- matrix of shape (n_C, n_H*n_W)\n",
    "    \n",
    "    Returns:\n",
    "    GA -- Gram matrix of A, of shape (n_C, n_C)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈1 line)\n",
    "    GA = tf.matmul(A,tf.transpose(A))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return GA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GA = [[  6.42230511  -4.42912197  -2.09668207]\n",
      " [ -4.42912197  19.46583748  19.56387138]\n",
      " [ -2.09668207  19.56387138  20.6864624 ]]\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "with tf.Session() as test:\n",
    "    tf.set_random_seed(1)\n",
    "    A = tf.random_normal([3, 2*1], mean=1, stddev=4)\n",
    "    GA = gram_matrix(A)\n",
    "    \n",
    "    print(\"GA = \" + str(GA.eval()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **GA**\n",
    "        </td>\n",
    "        <td>\n",
    "           [[  6.42230511  -4.42912197  -2.09668207] <br>\n",
    " [ -4.42912197  19.46583748  19.56387138] <br>\n",
    " [ -2.09668207  19.56387138  20.6864624 ]]\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.2 - Style cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After generating the Style matrix (Gram matrix), your goal will be to minimize the distance between the Gram matrix of the \"style\" image S and that of the \"generated\" image G. For now, we are using only a single hidden layer $a^{[l]}$, and the corresponding style cost for this layer is defined as: \n",
    "\n",
    "$$J_{style}^{[l]}(S,G) = \\frac{1}{4 \\times {n_C}^2 \\times (n_H \\times n_W)^2} \\sum _{i=1}^{n_C}\\sum_{j=1}^{n_C}(G^{(S)}_{ij} - G^{(G)}_{ij})^2\\tag{2} $$\n",
    "\n",
    "where $G^{(S)}$ and $G^{(G)}$ are respectively the Gram matrices of the \"style\" image and the \"generated\" image, computed using the hidden layer activations for a particular hidden layer in the network.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Compute the style cost for a single layer. \n",
    "\n",
    "**Instructions**: The 3 steps to implement this function are:\n",
    "1. Retrieve dimensions from the hidden layer activations a_G: \n",
    "    - To retrieve dimensions from a tensor X, use: `X.get_shape().as_list()`\n",
    "2. Unroll the hidden layer activations a_S and a_G into 2D matrices, as explained in the picture above.\n",
    "    - You may find [Hint1](https://www.tensorflow.org/versions/r1.3/api_docs/python/tf/transpose) and [Hint2](https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/reshape) useful.\n",
    "3. Compute the Style matrix of the images S and G. (Use the function you had previously written.) \n",
    "4. Compute the Style cost:\n",
    "    - You may find [Hint3](https://www.tensorflow.org/api_docs/python/tf/reduce_sum), [Hint4](https://www.tensorflow.org/api_docs/python/tf/square) and [Hint5](https://www.tensorflow.org/api_docs/python/tf/subtract) useful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: compute_layer_style_cost\n",
    "\n",
    "def compute_layer_style_cost(a_S, a_G):\n",
    "    \"\"\"\n",
    "    Arguments:\n",
    "    a_S -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image S \n",
    "    a_G -- tensor of dimension (1, n_H, n_W, n_C), hidden layer activations representing style of the image G\n",
    "    \n",
    "    Returns: \n",
    "    J_style_layer -- tensor representing a scalar value, style cost defined above by equation (2)\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ###\n",
    "    # Retrieve dimensions from a_G (≈1 line)\n",
    "    m, n_H, n_W, n_C = a_G.get_shape().as_list()\n",
    "    \n",
    "    # Reshape the images to have them of shape (n_C, n_H*n_W) (≈2 lines)\n",
    "    a_S = tf.reshape(a_S,(n_H*n_W,n_C))\n",
    "    a_G = tf.reshape(a_G,(n_H*n_W,n_C))\n",
    "\n",
    "    # Computing gram_matrices for both images S and G (≈2 lines)\n",
    "    GS = gram_matrix(tf.transpose(a_S))\n",
    "    GG = gram_matrix(tf.transpose(a_G))\n",
    "    \n",
    "    # Computing the loss (≈1 line)\n",
    "    J_style_layer =1/(4 * (n_C **2) * ((n_H * n_W) **2)) * tf.reduce_sum(tf.square(tf.subtract(GS,GG))) \n",
    "    \n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return J_style_layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J_style_layer = 9.19028\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "with tf.Session() as test:\n",
    "    tf.set_random_seed(1)\n",
    "    a_S = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n",
    "    a_G = tf.random_normal([1, 4, 4, 3], mean=1, stddev=4)\n",
    "    J_style_layer = compute_layer_style_cost(a_S, a_G)\n",
    "    \n",
    "    print(\"J_style_layer = \" + str(J_style_layer.eval()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **J_style_layer**\n",
    "        </td>\n",
    "        <td>\n",
    "           9.19028\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2.3 Style Weights\n",
    "\n",
    "So far you have captured the style from only one layer. We'll get better results if we \"merge\" style costs from several different layers. After completing this exercise, feel free to come back and experiment with different weights to see how it changes the generated image $G$. But for now, this is a pretty reasonable default: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "STYLE_LAYERS = [\n",
    "    ('conv1_1', 0.2),\n",
    "    ('conv2_1', 0.2),\n",
    "    ('conv3_1', 0.2),\n",
    "    ('conv4_1', 0.2),\n",
    "    ('conv5_1', 0.2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can combine the style costs for different layers as follows:\n",
    "\n",
    "$$J_{style}(S,G) = \\sum_{l} \\lambda^{[l]} J^{[l]}_{style}(S,G)$$\n",
    "\n",
    "where the values for $\\lambda^{[l]}$ are given in `STYLE_LAYERS`. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've implemented a compute_style_cost(...) function. It simply calls your `compute_layer_style_cost(...)` several times, and weights their results using the values in `STYLE_LAYERS`. Read over it to make sure you understand what it's doing. \n",
    "\n",
    "<!-- \n",
    "2. Loop over (layer_name, coeff) from STYLE_LAYERS:\n",
    "        a. Select the output tensor of the current layer. As an example, to call the tensor from the \"conv1_1\" layer you would do: out = model[\"conv1_1\"]\n",
    "        b. Get the style of the style image from the current layer by running the session on the tensor \"out\"\n",
    "        c. Get a tensor representing the style of the generated image from the current layer. It is just \"out\".\n",
    "        d. Now that you have both styles. Use the function you've implemented above to compute the style_cost for the current layer\n",
    "        e. Add (style_cost x coeff) of the current layer to overall style cost (J_style)\n",
    "3. Return J_style, which should now be the sum of the (style_cost x coeff) for each layer.\n",
    "!--> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_style_cost(model, STYLE_LAYERS):\n",
    "    \"\"\"\n",
    "    Computes the overall style cost from several chosen layers\n",
    "    \n",
    "    Arguments:\n",
    "    model -- our tensorflow model\n",
    "    STYLE_LAYERS -- A python list containing:\n",
    "                        - the names of the layers we would like to extract style from\n",
    "                        - a coefficient for each of them\n",
    "    \n",
    "    Returns: \n",
    "    J_style -- tensor representing a scalar value, style cost defined above by equation (2)\n",
    "    \"\"\"\n",
    "    \n",
    "    # initialize the overall style cost\n",
    "    J_style = 0\n",
    "\n",
    "    for layer_name, coeff in STYLE_LAYERS:\n",
    "\n",
    "        # Select the output tensor of the currently selected layer\n",
    "        out = model[layer_name]\n",
    "\n",
    "        # Set a_S to be the hidden layer activation from the layer we have selected, by running the session on out\n",
    "        a_S = sess.run(out)\n",
    "\n",
    "        # Set a_G to be the hidden layer activation from same layer. Here, a_G references model[layer_name] \n",
    "        # and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that\n",
    "        # when we run the session, this will be the activations drawn from the appropriate layer, with G as input.\n",
    "        a_G = out\n",
    "        \n",
    "        # Compute style_cost for the current layer\n",
    "        J_style_layer = compute_layer_style_cost(a_S, a_G)\n",
    "\n",
    "        # Add coeff * J_style_layer of this layer to overall style cost\n",
    "        J_style += coeff * J_style_layer\n",
    "\n",
    "    return J_style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: In the inner-loop of the for-loop above, `a_G` is a tensor and hasn't been evaluated yet. It will be evaluated and updated at each iteration when we run the TensorFlow graph in model_nn() below.\n",
    "\n",
    "<!-- \n",
    "How do you choose the coefficients for each layer? The deeper layers capture higher-level concepts, and the features in the deeper layers are less localized in the image relative to each other. So if you want the generated image to softly follow the style image, try choosing larger weights for deeper layers and smaller weights for the first layers. In contrast, if you want the generated image to strongly follow the style image, try choosing smaller weights for deeper layers and larger weights for the first layers\n",
    "!-->\n",
    "\n",
    "\n",
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The style of an image can be represented using the Gram matrix of a hidden layer's activations. However, we get even better results combining this representation from multiple different layers. This is in contrast to the content representation, where usually using just a single hidden layer is sufficient.\n",
    "- Minimizing the style cost will cause the image $G$ to follow the style of the image $S$. \n",
    "</font color='blue'>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 - Defining the total cost to optimize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's create a cost function that minimizes both the style and the content cost. The formula is: \n",
    "\n",
    "$$J(G) = \\alpha J_{content}(C,G) + \\beta J_{style}(S,G)$$\n",
    "\n",
    "**Exercise**: Implement the total cost function which includes both the content cost and the style cost. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: total_cost\n",
    "\n",
    "def total_cost(J_content, J_style, alpha = 10, beta = 40):\n",
    "    \"\"\"\n",
    "    Computes the total cost function\n",
    "    \n",
    "    Arguments:\n",
    "    J_content -- content cost coded above\n",
    "    J_style -- style cost coded above\n",
    "    alpha -- hyperparameter weighting the importance of the content cost\n",
    "    beta -- hyperparameter weighting the importance of the style cost\n",
    "    \n",
    "    Returns:\n",
    "    J -- total cost as defined by the formula above.\n",
    "    \"\"\"\n",
    "    \n",
    "    ### START CODE HERE ### (≈1 line)\n",
    "    J = alpha * J_content + beta * J_style\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    return J"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "J = 35.34667875478276\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "with tf.Session() as test:\n",
    "    np.random.seed(3)\n",
    "    J_content = np.random.randn()    \n",
    "    J_style = np.random.randn()\n",
    "    J = total_cost(J_content, J_style)\n",
    "    print(\"J = \" + str(J))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **J**\n",
    "        </td>\n",
    "        <td>\n",
    "           35.34667875478276\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>\n",
    "**What you should remember**:\n",
    "- The total cost is a linear combination of the content cost $J_{content}(C,G)$ and the style cost $J_{style}(S,G)$\n",
    "- $\\alpha$ and $\\beta$ are hyperparameters that control the relative weighting between content and style"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Solving the optimization problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's put everything together to implement Neural Style Transfer!\n",
    "\n",
    "\n",
    "Here's what the program will have to do:\n",
    "<font color='purple'>\n",
    "\n",
    "1. Create an Interactive Session\n",
    "2. Load the content image \n",
    "3. Load the style image\n",
    "4. Randomly initialize the image to be generated \n",
    "5. Load the VGG16 model\n",
    "7. Build the TensorFlow graph:\n",
    "    - Run the content image through the VGG16 model and compute the content cost\n",
    "    - Run the style image through the VGG16 model and compute the style cost\n",
    "    - Compute the total cost\n",
    "    - Define the optimizer and the learning rate\n",
    "8. Initialize the TensorFlow graph and run it for a large number of iterations, updating the generated image at every step.\n",
    "\n",
    "</font>\n",
    "Lets go through the individual steps in detail. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You've previously implemented the overall cost $J(G)$. We'll now set up TensorFlow to optimize this with respect to $G$. To do so, your program has to reset the graph and use an \"[Interactive Session](https://www.tensorflow.org/api_docs/python/tf/InteractiveSession)\". Unlike a regular session, the \"Interactive Session\" installs itself as the default session to build a graph.  This allows you to run variables without constantly needing to refer to the session object, which simplifies the code.  \n",
    "\n",
    "Lets start the interactive session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reset the graph\n",
    "tf.reset_default_graph()\n",
    "\n",
    "# Start interactive session\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load, reshape, and normalize our \"content\" image (the Louvre museum picture):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "content_image = scipy.misc.imread(\"images/cat.jpg\")\n",
    "content_image = reshape_and_normalize_image(content_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's load, reshape and normalize our \"style\" image (Claude Monet's painting):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: `imread` is deprecated!\n",
      "`imread` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n",
      "Use ``imageio.imread`` instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "style_image = scipy.misc.imread(\"images/camp-nou.jpg\")\n",
    "style_image = reshape_and_normalize_image(style_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we initialize the \"generated\" image as a noisy image created from the content_image. By initializing the pixels of the generated image to be mostly noise but still slightly correlated with the content image, this will help the content of the \"generated\" image more rapidly match the content of the \"content\" image. (Feel free to look in `nst_utils.py` to see the details of `generate_noise_image(...)`; to do so, click \"File-->Open...\" at the upper-left corner of this Jupyter notebook.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "generated_image = generate_noise_image(content_image)\n",
    "#imshow(generated_image[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, as explained in part (2), let's load the VGG16 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_vgg_model(\"pretrained-model/imagenet-vgg-verydeep-19.mat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the program to compute the content cost, we will now assign `a_C` and `a_G` to be the appropriate hidden layer activations. We will use layer `conv4_2` to compute the content cost. The code below does the following:\n",
    "\n",
    "1. Assign the content image to be the input to the VGG model.\n",
    "2. Set a_C to be the tensor giving the hidden layer activation for layer \"conv4_2\".\n",
    "3. Set a_G to be the tensor giving the hidden layer activation for the same layer. \n",
    "4. Compute the content cost using a_C and a_G."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign the content image to be the input of the VGG model.  \n",
    "sess.run(model['input'].assign(content_image))\n",
    "\n",
    "# Select the output tensor of layer conv4_2\n",
    "out = model['conv4_2']\n",
    "\n",
    "# Set a_C to be the hidden layer activation from the layer we have selected\n",
    "a_C = sess.run(out)\n",
    "\n",
    "# Set a_G to be the hidden layer activation from same layer. Here, a_G references model['conv4_2'] \n",
    "# and isn't evaluated yet. Later in the code, we'll assign the image G as the model input, so that\n",
    "# when we run the session, this will be the activations drawn from the appropriate layer, with G as input.\n",
    "a_G = out\n",
    "\n",
    "# Compute the content cost\n",
    "J_content = compute_content_cost(a_C, a_G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: At this point, a_G is a tensor and hasn't been evaluated. It will be evaluated and updated at each iteration when we run the Tensorflow graph in model_nn() below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign the input of the model to be the \"style\" image \n",
    "sess.run(model['input'].assign(style_image))\n",
    "\n",
    "# Compute the style cost\n",
    "J_style = compute_style_cost(model, STYLE_LAYERS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Now that you have J_content and J_style, compute the total cost J by calling `total_cost()`. Use `alpha = 10` and `beta = 40`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "### START CODE HERE ### (1 line)\n",
    "J = total_cost(J_content,J_style,10,40)\n",
    "### END CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'd previously learned how to set up the Adam optimizer in TensorFlow. Lets do that here, using a learning rate of 2.0.  [See reference](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define optimizer (1 line)\n",
    "optimizer = tf.train.AdamOptimizer(2.0)\n",
    "\n",
    "# define train_step (1 line)\n",
    "train_step = optimizer.minimize(J)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Implement the model_nn() function which initializes the variables of the tensorflow graph, assigns the input image (initial generated image) as the input of the VGG16 model and runs the train_step for a large number of steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_nn(sess, input_image, num_iterations = 200):\n",
    "    \n",
    "    # Initialize global variables (you need to run the session on the initializer)\n",
    "    ### START CODE HERE ### (1 line)\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Run the noisy input image (initial generated image) through the model. Use assign().\n",
    "    ### START CODE HERE ### (1 line)\n",
    "    generated_image = sess.run(model[\"input\"].assign(input_image))\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    for i in range(num_iterations):\n",
    "    \n",
    "        # Run the session on the train_step to minimize the total cost\n",
    "        ### START CODE HERE ### (1 line)\n",
    "        sess.run(train_step)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Compute the generated image by running the session on the current model['input']\n",
    "        ### START CODE HERE ### (1 line)\n",
    "        generated_image = sess.run(model[\"input\"])\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Print every 20 iteration.\n",
    "        if i%20 == 0:\n",
    "            Jt, Jc, Js = sess.run([J, J_content, J_style])\n",
    "            print(\"Iteration \" + str(i) + \" :\")\n",
    "            print(\"total cost = \" + str(Jt))\n",
    "            print(\"content cost = \" + str(Jc))\n",
    "            print(\"style cost = \" + str(Js))\n",
    "            \n",
    "            # save current generated image in the \"/output\" directory\n",
    "            save_image(\"output/\" + str(i) + \".png\", generated_image)\n",
    "    \n",
    "    # save last generated image\n",
    "    save_image('output/generated_image.jpg', generated_image)\n",
    "    \n",
    "    return generated_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to generate an artistic image. It should take about 3min on CPU for every 20 iterations but you start observing attractive results after ≈140 iterations. Neural Style Transfer is generally trained using GPUs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0 :\n",
      "total cost = 3.378e+09\n",
      "content cost = 6779.21\n",
      "style cost = 8.44484e+07\n",
      "Iteration 20 :\n",
      "total cost = 6.06578e+08\n",
      "content cost = 14132.3\n",
      "style cost = 1.51609e+07\n",
      "Iteration 40 :\n",
      "total cost = 2.49599e+08\n",
      "content cost = 15234.1\n",
      "style cost = 6.23617e+06\n",
      "Iteration 60 :\n",
      "total cost = 1.34311e+08\n",
      "content cost = 16017.6\n",
      "style cost = 3.35378e+06\n",
      "Iteration 80 :\n",
      "total cost = 9.01261e+07\n",
      "content cost = 16375.0\n",
      "style cost = 2.24906e+06\n",
      "Iteration 100 :\n",
      "total cost = 6.92042e+07\n",
      "content cost = 16545.0\n",
      "style cost = 1.72597e+06\n",
      "Iteration 120 :\n",
      "total cost = 5.68947e+07\n",
      "content cost = 16688.5\n",
      "style cost = 1.41819e+06\n",
      "Iteration 140 :\n",
      "total cost = 4.86958e+07\n",
      "content cost = 16806.0\n",
      "style cost = 1.21319e+06\n",
      "Iteration 160 :\n",
      "total cost = 4.28935e+07\n",
      "content cost = 16923.6\n",
      "style cost = 1.06811e+06\n",
      "Iteration 180 :\n",
      "total cost = 3.84857e+07\n",
      "content cost = 17036.0\n",
      "style cost = 957884.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[[[ -36.16042709,  -10.5479002 ,  -16.83213234],\n",
       "         [  50.42149734,   28.43038177,   59.47467041],\n",
       "         [  39.37503815,   29.13621521,    3.15614247],\n",
       "         ..., \n",
       "         [  22.05049896,   97.80631256,   57.5896759 ],\n",
       "         [ -21.67116356,   97.12916565,   21.16929817],\n",
       "         [ -93.48135376,  -22.81381226,  -12.87886429]],\n",
       "\n",
       "        [[ -78.36818695,  -28.19713402,  -31.60643387],\n",
       "         [  42.77455521,   48.09644318,   62.23609924],\n",
       "         [  51.47885513,   51.64861298,   74.39982605],\n",
       "         ..., \n",
       "         [  46.26527786,  130.73312378,   90.97173309],\n",
       "         [  46.51071548,  100.68495941,  103.91737366],\n",
       "         [  20.83648872,  210.69329834,   24.06349182]],\n",
       "\n",
       "        [[ -93.80913544,  -41.61068726,  -35.17567825],\n",
       "         [  -8.10842228,  -30.97846222,  -41.10457611],\n",
       "         [  -6.04685259,    4.45813894,  -22.9856739 ],\n",
       "         ..., \n",
       "         [  65.70767975,   90.01563263,   76.52348328],\n",
       "         [  53.42876053,   72.96408081,  182.17951965],\n",
       "         [  59.70411682,   86.51517487,  182.07437134]],\n",
       "\n",
       "        ..., \n",
       "        [[  10.33844852,    3.01373863,  146.026474  ],\n",
       "         [ -88.36104584,  -62.70292664,   76.65638733],\n",
       "         [ -65.91890717,  -86.41455841,   42.452034  ],\n",
       "         ..., \n",
       "         [  26.85643196,   46.23838043,   38.80353928],\n",
       "         [  36.12872696,   60.56376266,   72.28250122],\n",
       "         [  54.17956161,   65.1802063 ,   66.211586  ]],\n",
       "\n",
       "        [[ -40.14914703,  -63.30712509,   48.67811966],\n",
       "         [-102.49437714, -106.5047226 ,  -69.14276886],\n",
       "         [ -57.79241562, -111.13700867,  -53.71138382],\n",
       "         ..., \n",
       "         [  58.4585228 ,   61.52036285,   58.14968872],\n",
       "         [  63.92447281,   57.24223328,   58.78475952],\n",
       "         [  47.21494675,   62.90480804,   69.3052597 ]],\n",
       "\n",
       "        [[  -9.87302589,  -75.25294495,  101.34101868],\n",
       "         [ -33.95248032, -153.92001343,   10.20735359],\n",
       "         [ -20.26525116,  -83.91119385,   26.33274841],\n",
       "         ..., \n",
       "         [  94.82893372,   90.60083008,   50.63745117],\n",
       "         [  75.85122681,   75.05908966,  114.14029694],\n",
       "         [  87.15032959,   74.04200745,  107.38037109]]]], dtype=float32)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_nn(sess, generated_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table>\n",
    "    <tr>\n",
    "        <td>\n",
    "            **Iteration 0 : **\n",
    "        </td>\n",
    "        <td>\n",
    "           total cost = 5.05035e+09 <br>\n",
    "           content cost = 7877.67 <br>\n",
    "           style cost = 1.26257e+08\n",
    "        </td>\n",
    "    </tr>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You're done! After running this, in the upper bar of the notebook click on \"File\" and then \"Open\". Go to the \"/output\" directory to see all the saved images. Open \"generated_image\" to see the generated image! :)\n",
    "\n",
    "You should see something the image presented below on the right:\n",
    "\n",
    "<img src=\"images/louvre_generated.png\" style=\"width:800px;height:300px;\">\n",
    "\n",
    "We didn't want you to wait too long to see an initial result, and so had set the hyperparameters accordingly. To get the best looking results, running the optimization algorithm longer (and perhaps with a smaller learning rate) might work better. After completing and submitting this assignment, we encourage you to come back and play more with this notebook, and see if you can generate even better looking images. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are few other examples:\n",
    "\n",
    "- The beautiful ruins of the ancient city of Persepolis (Iran) with the style of Van Gogh (The Starry Night)\n",
    "<img src=\"images/perspolis_vangogh.png\" style=\"width:750px;height:300px;\">\n",
    "\n",
    "- The tomb of Cyrus the great in Pasargadae with the style of a Ceramic Kashi from Ispahan.\n",
    "<img src=\"images/pasargad_kashi.png\" style=\"width:750px;height:300px;\">\n",
    "\n",
    "- A scientific study of a turbulent fluid with the style of a abstract blue fluid painting.\n",
    "<img src=\"images/circle_abstract.png\" style=\"width:750px;height:300px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - Test with your own image (Optional/Ungraded)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can also rerun the algorithm on your own images! \n",
    "\n",
    "To do so, go back to part 4 and change the content image and style image with your own pictures. In detail, here's what you should do:\n",
    "\n",
    "1. Click on \"File -> Open\" in the upper tab of the notebook\n",
    "2. Go to \"/images\" and upload your images (requirement: (WIDTH = 300, HEIGHT = 225)), rename them \"my_content.png\" and \"my_style.png\" for example.\n",
    "3. Change the code in part (3.4) from :\n",
    "```python\n",
    "content_image = scipy.misc.imread(\"images/louvre.jpg\")\n",
    "style_image = scipy.misc.imread(\"images/claude-monet.jpg\")\n",
    "```\n",
    "to:\n",
    "```python\n",
    "content_image = scipy.misc.imread(\"images/my_content.jpg\")\n",
    "style_image = scipy.misc.imread(\"images/my_style.jpg\")\n",
    "```\n",
    "4. Rerun the cells (you may need to restart the Kernel in the upper tab of the notebook).\n",
    "\n",
    "You can also tune your hyperparameters: \n",
    "- Which layers are responsible for representing the style? STYLE_LAYERS\n",
    "- How many iterations do you want to run the algorithm? num_iterations\n",
    "- What is the relative weighting between content and style? alpha/beta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 - Conclusion\n",
    "\n",
    "Great job on completing this assignment! You are now able to use Neural Style Transfer to generate artistic images. This is also your first time building a model in which the optimization algorithm updates the pixel values rather than the neural network's parameters. Deep learning has many different types of models and this is only one of them! \n",
    "\n",
    "<font color='blue'>\n",
    "What you should remember:\n",
    "- Neural Style Transfer is an algorithm that given a content image C and a style image S can generate an artistic image\n",
    "- It uses representations (hidden layer activations) based on a pretrained ConvNet. \n",
    "- The content cost function is computed using one hidden layer's activations.\n",
    "- The style cost function for one layer is computed using the Gram matrix of that layer's activations. The overall style cost function is obtained using several hidden layers.\n",
    "- Optimizing the total cost function results in synthesizing new images. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was the final programming exercise of this course. Congratulations--you've finished all the programming exercises of this course on Convolutional Networks! We hope to also see you in Course 5, on Sequence models! \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### References:\n",
    "\n",
    "The Neural Style Transfer algorithm was due to Gatys et al. (2015). Harish Narayanan and Github user \"log0\" also have highly readable write-ups from which we drew inspiration. The pre-trained network used in this implementation is a VGG network, which is due to Simonyan and Zisserman (2015). Pre-trained weights were from the work of the MathConvNet team. \n",
    "\n",
    "- Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, (2015). A Neural Algorithm of Artistic Style (https://arxiv.org/abs/1508.06576) \n",
    "- Harish Narayanan, Convolutional neural networks for artistic style transfer. https://harishnarayanan.org/writing/artistic-style-transfer/\n",
    "- Log0, TensorFlow Implementation of \"A Neural Algorithm of Artistic Style\". http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style\n",
    "- Karen Simonyan and Andrew Zisserman (2015). Very deep convolutional networks for large-scale image recognition (https://arxiv.org/pdf/1409.1556.pdf)\n",
    "- MatConvNet. http://www.vlfeat.org/matconvnet/pretrained/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "convolutional-neural-networks",
   "graded_item_id": "owWbQ",
   "launcher_item_id": "lEthw"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
